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```ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 02, August 201 1

Evolutionary Algorithmical Approach for VLSI
Physical Design- Placement Problem

Varatharajan .R ', Perumal Sankar.S 2 , Lekha.R 3 , Kumar avel 4

1 Bharath University,Department of ECE,Chennai,India

E-mail :varathu21 @ !yahoo.com

2 Cape Institute of Technology,Department of ECE,Kanyakumari, India

E-mail: spsankar2004 @ yahoo, co. in

3 Bharath UniversityDepartment of ECE,Chennai,India

E-mail:lekhavathy@gmail.com

4 Bharath UniversityDepartment of CSC,Chennai,India

E-mail :drkumaravel @ gmail. com

Abstract:-Phjsical layout automation is very important in
VLSI's field. With the advancement of semiconductor
technology, VLSI is coming to VDSM (Very Deep Sub
Micrometer), and the scale of the random logic IC circuits
goes towards million gates. Physical design is the process of
determining the physical location of active devices and
interconnecting them inside the boundary of the VLSI
chip.The earliest and the most critical stage in VLSI layout
design is the placement. The background is the rectangle
packing problem: given a set of rectangular modules of
arbitrary sizes, place them without overlap on a plane within
a rectangle of minimum area [1], [5]. The VLSI placement
problem is to place the object in the fixed area of die without
overlap and with some cost constrain such as the wire length
and area of the die. The wire length and the area optimization
is the major task in the physical design. We first
introduce about the major technique involved in the algorithm.

Index Terms: Placement problems, Memetic algorithm, wire
length minimization, area minimization.

I .Introduction

The task of the very large scale integration (VLSI)
placement is to assign exact location to various circuit
components within the chip area. It involves number of
objectives such as wire length, area of the die, timing and
power. Placement treats the shapes of all blocks as fixed, i.e.,
it only determines the location of each block on the chip. The
variables are the xy locations of the blocks; most blocks are
standard cells. The y-locations of cells are restricted to
standard- cell rows. Placement instance sizes range in to the
tens of millions and will continue to increase. Placement is
usually divided into two steps: global placement and detailed
placement [1]. Global placement assigns blocks to certain
sub regions of the chip with out determining the exact location
of each component within its sub region. As a result, the
blocks may still overlap. Detailed placement starts from the
result of global placement, removes all overlap between
blocks, and further optimizes the design. Placement objectives
include the estimated total wire length needed to connect
blocks in nets, the maximum expected wiring congestion in
subsequent routing and the timing performances of the
circuit. To solve such large scale mixed size VLSI placement
problem many algorithms are used in VLSI. Among these
algorithms memetic algorithm is used to solve the placement

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problem considered here. The placement algorithms are
classified into constructive and iterative improvement methods
[3]. The constructive algorithm starts with the empty set and
builds up the partition by adding one element at a time. These
algorithms are faster but the quality of result is not good. An
iterative algorithm starts with initial placements and repeatedly
modifies it. These algorithms give good result but takes long
time. The placement problems are the wire length [2] and area
of the die [3], routability, power minimization and delay [2].
Out of these above mentioned problems the area minimization
and the wire length minimization are the most critical part. For
the area and wire length optimization a modern placer needs
to handle the large-scale design with millions of objects,
heterogeneous objects with different sizes and various
constrained placement such as preplaced blocks and chip
density. The traditional approach in placement is to construct
an initial solution by using constructive heuristic algorithms.
A final solution is then produced by using iterative
improvement techniques where a modification is usually
accepted if a reduction in cost occurs, otherwise it is rejected.
The solution generated by constructive algorithms may be
far from optimal. Thus an iterative improvement algorithm is
performed next to improve the solution and the computation
time of such algorithm is also large. For many combinatorial
optimization problems, no effective algorithms capable of
finding guaranteed optimum solutions in short time are
available. Therefore, powerful heuristics have been developed
that deliver the guarantee optimum solution, but have shown
to be highly effective in many test cases. A special class of
heuristics is investigated. The algorithms under consideration
are called memetic algorithms, which are - roughly speaking -
hybrids of evolutionary algorithms and problem-specific
search algorithms, such as greedy heuristics and local search.
Memetic algorithms (MAs) are evolutionary algorithms (EAs)
that apply the local search process to refine the individual
MAs include a broad of metaheuristics [5] ,[4]. This method is
based on the population of agents and proved to be of practical
success in a variety of problem domains. We can be sure that
MAs constitute one of the most successful approaches for
combinatorial optimization in general and for the approximate
solution of NP optimization problems. The reminder of this
paper is organized as follows: section 2 gives the memetic
algorithm flow, Section 3 involves the equation in the

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placement problem and Section 4 shows the experiment result
for the problem considered.

II. Memetic Algorithms

Memetic Algorithms are class of stochastic global search
heuristics in which Evolutionary Algorithm based approaches
are combined with problem-specific solvers. Later local search
heuristics techniques are implemented. This hybridisation is
to either accelerate or to discover good solution from the
population where the evolution alone would take long time
to discover or to reach the solution. Memetic Algorithms use
heuristic local searches either approximate method or exact
method to get the local refined solution from the population.
There are several reasons for hybridising evolutionary
algorithms with local searchers. Some of them are mentioned
below:

1. Complex problems are decomposed to different sub
problems could be better solved by different methods.

2. The hybridisation of evolutionary algorithm with local
search algorithm result in fine tuning or repairing the best
solution(s) produced by the evolutionary algorithm. The
powerful local searcher introduces diversity into the solution.

3. The sub problems are dealt by the various operators,
for example cross over and mutation or by the local searcher.
Thus the search process is done with in the search space
region.

4. In some cases the available exact or approximate
methods for the sub problems are included with the
evolutionary algorithm.

5. The heuristic or local search is used to repair the
solutions found by the evolutionary algorithm. The heuristic
or local search do the same in the Memetic algorithm. Such
heuristic are called Meteheuristic algorithm.

A. Memetic algorithm flow

Memetic algorithms try to simulate cultural evolution
rather than the biological one, as evolutionary algorithm do.
They are a combination of population based global
optimization and heuristic local search. First individuals are
initialized randomly. Starting with each single individual, one
local search is performed. After that, the individuals start to
interact either competitively (in selection form) or by
cooperatively (in recombination form). These two steps are
repeated until the stopping criterion is met. From this context
of above we can know that memetic algorithm acts as two
level optimization algorithm, the top level algorithm is an
evolutionary algorithm otherwise a population based
algorithm and at the bottom levels a single individual
optimizer like hill climbing or simulated annealing or some
other local search algorithm.

B. Evolutionary Algorithms + Local Search = Memetic
Algorithms

Combining global and local search algorithm used for
many hybrid optimization approaches. Memetic algorithms
(MAs) are Evolutionary Algorithms (EAs) and along with
that we apply some sort of local search algorithm to further
improve the fitness of individual in the population. Memetic

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Algorithms have been shown to be very effective in solving
many hard combinatorial optimization problems. The
approach combines a hierarchical design technique, Genetic
Algorithms, constructive algorithms and advance local search
to solve the VLSI layout in various steps like partitioning
and placement. The efficient optimization algorithm used to
solve hard problems usually employs a hybrid of at least two
techniques to find a near optimal solution to problem
considered. The main motivation for this hybridisation is to
increase the efficiency that is to get the good quality solution
in specified time.

Initial population

I

Mating pool

T

Selection

t

Cross over

f

Offspring

Mutation

Offspring

Figure 1 . Algorithm Template

The above figure identifies the Memetic algorithm
template. The structure is the basic evolutionary algorithm
structure and the black mark placed on the particular block is
to mention that hybridisation takes place in that block. Each
of the black mark provides an opportunity for hybridisation.
For example the initial population could be seed with
solutions arising from sophisticated problem for specific
heuristics. The cross over and mutation operator could be
enhanced with domain specific and representation specific
constrain as to provide better search ability to Evolutionary
Algorithm. More over local search could be applied to any or
all of the intermediate set of solution, for example, offspring
set. The more problem specific knowledge is incorporated
with in a Memetic algorithm the better it will perform. The
most popular form of hybridisations is to apply one or more
phase of local search, based on some probability parameter,
to individual members of population in each generation.

C. Memetic Algorithm for Circuit Placement

The placement is nothing but arranging the circuit
components in layout. In general-cell and standard-cell

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ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 02, August 201 1

placement, we have to position the components of the circuit
such that the layout area is minimized. The area measure
used here comprises the area taken up by the circuit
components as well as the area needed for wiring the circuit
components. The placement problem has the dual flavour of
a two dimensional packing problem and connection cost
optimization problem. The packing problem is concerned
with fitting a number of cells of different sizes and shapes
tightly into a rectangle. The connection cost optimization
aims at minimizing the amount of wire that is needed. The
proposed Memetic Algorithm for circuit placement [4] [5] is
based on the Genetic Algorithm . In each generation, a Tile-
based local search heuristic is performed on part of the
population to improve their fitness. The local search maybe
introduced in genetic algorithm to offspring population, it
may be in cross over or else where. For the circuit placement
problem, the pure Genetic Algorithm is combined with Tile-
based local search in three different ways, referred to as
performing local search on part of the population: (i) before
the crossover (ii)after the crossover (iii) before and after the
crossover. [1].

1. Encode solution space for placement

2. Set population size, max-gen, generation=0;
Set crossover-rate, mutation-rate;

3. Generate the initial population

4. Evaluate the initial population

5. While (generation < max-gen)

Apply genetic algorithm

Apply tile-based local search to population
End while

6. Return the best solution in current population

Figure 2. Pseudocode for Memetic placement algorithm

A Memetic algorithm for the circuit placement is purely
based on the Genetic algorithm. From the above figure we can
under stand that the memetic algorithm for the circuit place-
ment starts with the initial random population generation
before that we have to set the size of the population , maxi-
mum number of generation, cross over rate, and mutation rate.
Next we apply the Genetic algorithm, the genetic algorithm
on the fitness function. The chromosome for the cross over is
selected using this fitness function . Cross over is the base
point for the next generation population. The cross over tech-
nique used in Genetic algorithm is one point cross over, two
point cross over, cut and splice cross over , uniform cross
over and half uniform cross over. After the cross over, the
mutation process is done to maintain the genetic diversity
from one generation to next generation. In this mutation the
genetic sequence will be changed from its original by gener-
ating a random variable for each bit sequence. After the muta-
tion, accepting offspring is placed in the new population for
further iteration. The next step in memetic algorithm for cir-
cuit placement is to apply the local search algorithm in be-
tween this genetic algorithm. As mentioned before the local
search is applied in three ways as given below:
(i) Before the crossover
(ii) After the crossover

(iii) Before and after the crossover.

III. Placement Problem Description

A. Area Estimation

Given a set of module M„ 1VL, M and set of n

1' 2' n

interconnects N ,N, N the objective of the placement is

to obtain the non overlapping package of all the modules
which achieves some optimization objective such as
minimizing the area of package[3], the interconnection length
as shown in the figure below:

^ J

2

4

1

5

3

H

Figure 3. Six block placement

B. Horizontal Constrain:

If (-X,Y) =(. . . a. . .b . .,a. . . b. . .) Block b is is at right side
of the block a

C. Vertical Constrain:

If (X,Y)=(. . . a. . .b. . .,b.. . a. . .) Block b is at the below
side of the block a Based on "left of constraint of (X, Y), a
directed and vertex-weighted graph G H (V, E) (V: vertex set, E:
edge set), called the horizontal-constraint graph (HCG) is
constructed as follows:

£1) V= {S h } TJ {t h } U {bj H i = 1 = . . . , M},

Where b correspond to the block

S h is the source node representing the left boundary
L is the target node representing the right boundary

(2) E = {(S t: bi) i| = 1 , . . . M] U {(b i: ti) | i = 1 : . . . >!} U
«Kb|) ...K..^}

If existing, edge (b.,b. +1 ) edge (b. +1 ,b. +2 ) and edge (b.,b. +2 )
then (b.,b. ,) omitted

3) Vertex Weight equals the width of the block b but zero for
S h and t. similarly the vertical constrain graph (VGH) as show
in the below figure. Vertical constrain graph G (V,E) is
constructed using "above" constrain and the height of the
each block. As for the example show in the above figure. The
corresponding constrain graph G h (V,E) and G v (V,E) are as
show in the figures. Both G h (V,E) and G v (V,E) are vertex
weighted acyclic graph so longest path algorithm can be
applied to find the x and y co ordinates of each block.

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Figure 4. Example for vertical constrain graph (VGH)

The coordinates of the block coordinate of the lower left
corner of the block.

Figure 5. Example for horizontal constrain graph

D. Wire length estimation.

We are addressing the problem of VLSI standard Cell
placement with the objectives of minimizing wire length [2],
power consumption, and timing performance (delay), while
considering the layout width as a constraint.

E. Wire length measurement and Cost:

In VLSI placement the cells present in the module are
connected by wire. The estimation of wire length [2] required
for connection is calculated by the formula

Wire lcngth ="w ij ((x i -x/ + (y i -y/)

I>j

Where
w . weight of the connection between cell x and y

(x - x ) distance in X direction

v i /

(y - y ) distance in Y direction
Inter connect wire length of each net in the circuit is estimate
during Steiner tree and then total wire length is computed by

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Cost ="1

wire l

i€M
Where 1 is the wire length estimation for net i and M denotes
total number of nets in circuit.

IV Experimental Result

The Memetic algorithm for VLSI cell placement problem
was tested on real life circuits chosen from a benchmark suite
that for design work shop in early 1990's and it is often referring
to as MCNC benchmark. They were originally released and
maintained by North Carolina's Microelectronics, computing
and networking centre and now it got changed to CAD
Benchmarking Laboratory (CBL). All results of our Memetic
algorithm presented in this section were obtained by
implementation of Memetic algorithm for placement. Here
first the initial population is generated and the fitness function
is evaluated. Based on that fitness selection of parents for
the cross over. After this process the normal mutation and
inversion operation takes place, in addition to this process
for each sub population, the local search is applied to refine
the fitness of each individual to get the most optimal
solution.We are implementing our algorithm in c language as
(genetic algorithm + local search) , and the experiment is
executed on the Intel Pentium processor (3. 1GHz, 5 12 RAM)
machine running windows xp . The memetic algorithm (genetic
algorithm + local search) is embedded in our algorithm the
block placement. The results obtained by implementing our
algorithm are shown in the table below:

Table 1 .Performance statistics for MCNC Benchmark circuits

Psicmimce statistics for ftvsMCXC Benchmark circuits

Circuit

Cells

\"ets

Final iiire
length(mm)

Final chip are:
(mm")

aj;t;

9

97

590.6

61J

xerox

10

203

103 S

32.6

hp

11

33

365

42.9

Ami33

33

123

27S.5

1.23

Ami49

49

-::§

2077

V. Conclusions

The hybrid approach for the combinatorial placement
problem is memetic algorithm, this memetic algorithm may
also be called as hybrid genetic algorithm, here the
hybridisation is done in genetic algorithm with some local
search technique. First the memetic algorithm starts with the
genetic algorithm, finds the global minimum solution and to
further refine the individual and to get the local minimum we
introduce a local search. The main future of the approach
introduced here, in comparison with other approaches, is the
manner in which block flexibility is treated. During the
iteration several implementation such as wire length
calculation and area estimation are considered and some
shape functions are introduced for the block. The common
hill climbers in genetic algorithm for combinatorial optimization
problem randomly explore the solutions neighbouring the
candidate solution encoded in the current individual and
accept those with better fitness. In hybrid approaches, local
search techniques explore the solution space close to the
sample points by applying specialized heuristics. When

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ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 02, August 201 1

including problem specific knowledge during creation of
individual, as in our approach, it is possible to identify
unfavourable or redundant partial solutions and consider
only the most promising ones. Therefore, each individual in
our hybrid genetic algorithms encodes a set of high quality
solutions, the best of which is a local optimum. This paper
explores the memetic algorithm strategies for a multi objective
VLSI cell placement. The results produced by implementing
memetic algorithms are the most optimal solution compared
to other set of algorithm used for VLSI cell placement. In this
paper wire length estimation and area estimation were
considered. For future work: width cost, delay cost, power
cost and power minimization are the parameters that can be
considered.

Acknowledgment

It is to note here that this topic-specific article for the
easy reference of VLSI Placement Problem, optionally being
used together with the all the problems in VLSI
PhysicalDesign. Author Varatharajan, I thank the management
of Bharath University, India to conduct his research in this
area and also he worked under the Guidance of Dr.Perumal
sankar and Dr.Kumaravel , the Eminent Professors in India.
He thank the guide for his moral support to continue the
research.

References

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[2] Sadiq M. Sait, Mustafa Imran Ali, and Ali Mustafa Zaidi;
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[5] Bo Hu and Malgorzata Marek-Sadowska, "Multi level Fixed-
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